title: Improved Signal Processing Methods for Velocity Selective Neural Recording Using Multi-Electrode Cuffs.
abstract: This paper describes an improved system for obtaining velocity spectral information from electroneurogram recordings using multi-electrode cuffs (MECs). The starting point for this study is some recently published work that considers the limitations of conventional linear signal processing methods (`delay-and-add') with and without additive noise. By contrast to earlier linear methods, the present paper adopts a fundamentally non-linear velocity classification approach based on a type of artificial neural network (ANN). The new method provides a unified approach to the solution of the two main problems of the earlier delay-and-add technique, i.e., a damaging decline in both velocity selectivity and velocity resolution at high velocities. The new method can operate in real-time, is shown to be robust in the presence of noise and also to be relatively insensitive to the form of the action potential waveforms being classified.
authors.forename: Assad I K
authors.initials: AI
authors.lastname: Al-Shueli
authors.forename: Christopher T
authors.initials: CT
authors.lastname: Clarke
authors.forename: Nick
authors.initials: N
authors.lastname: Donaldson
authors.forename: John
authors.initials: J
authors.lastname: Taylor
mesh_terms: D000200:Action Potentials; D004566:Electrodes; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted